Research map / Graph learning / GNN architectures
$\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use aggregation-aware learned mixed-precision quantization instead of DQ-INT4 | GAT · Cora | accuracy: 71.2±2.9% -> 82.6±0.6% | Helped |
| Use aggregation-aware learned mixed-precision quantization instead of DQ-INT4 | GIN · CiteSeer | accuracy: 60.8±2.1% -> 65.1±1.7% | Helped |
| Use aggregation-aware learned mixed-precision quantization instead of DQ-INT4 | GIN · REDDIT-BINARY | accuracy: 81.3±4.4% -> 90.8±1.8% | Helped |
| Learn mixed-precision bitwidths rather than use uniform 4-bit quantization (ablation) | GCN · Cora | accuracy: +1.1% | Helped |
| Learn bitwidth assignments rather than manually assign them by node in-degree (ablation) | GIN · CiteSeer | accuracy: 21.5% higher accuracy | Helped |
| Learn the quantization step size (ablation) | GIN · Cora | accuracy | Helped |
| Learn bitwidth and step size simultaneously (ablation) | GIN · Cora | accuracy | Helped |
| Train node-feature quantization parameters with Local Gradient rather than Global Gradient (ablation) | GCN · CiteSeer | accuracy: 13.8% | Helped |
| Apply Nearest Neighbor Strategy to select quantization parameters for unseen graph nodes (ablation) | GIN · REDDIT-BINARY | accuracy: 19.3% accuracy gains | Helped |
| Apply Nearest Neighbor Strategy during inference (ablation) | GIN · REDDIT-BINARY | inference time: 121.45ms -> 122.60ms; 0.95% overhead | Hurt |
Closest papers by meaning
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